Supply Chain 2012 Part 8 ppt

Fuzzy Multiple Agent Decision Support Systems for Supply Chain Management 201 Figure 7. Cost comparison of Agent-Based model with simulation and 1-1 policy 12. Conclusions This chapter proposes a proper modular architecture for the information agent, based on the inputs, functions, and outputs of the agent, for supply chain management. The proposed architecture has nine different modules, each of which is responsible for one or more function(s) for the information agent. Then, we explored the occurrence of bullwhip effect in supply chains, in a fuzzy environment. We built an agent-based system which can operate in a fuzzy environment and is capable of managing the supply chain in a completely uncertain environment. They are able to track demands, remove the bullwhip effect almost completely, and discover policies under complex scenarios, where analytical solutions are not available. Such an automated supply chain is adaptable to an ever-changing businessenvironment. 13. 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Zarandi M.H.F., Turksen I.B., Saghiri S., (2005) “Fuzzy Multiple Objective Supplier Selection in Multiple Products and Supplier Environment”, International Journal of Engineering Science, Vol. 16, No. 2, pp. 1-20. 12 Align Agile Drivers, Capabilities and Providers to Achieve Agility: a Fuzzy-Logic QFD Approach Chwei-Shyong Tsai 1 and Chien-Wen Chen 2 and Ching-Torng Lin 3 1 Department of Management Information Systems, National Chung-Hsing University, Taichung 2 Department of Business Administration, Feng Chia University, Taichung 3 Department of Information Management, Da-Yeh University, Changhua Taiwan 1. Introduction At the beginning of the twenty-first century, the world faces profound changes in many aspects, especially marketing competition, technological innovations and customer demands. A world-wide dispersion of education and technology has led to intense and increasingly global competition and an accelerated rate of change in the marketplace and innovation. There is a continuing fragmentation of mass markets into niche markets, as customers become more demanding with their increasing expectations. This critical situation has led to major revisions in business priorities, strategic vision, and the viability of conventional and even relatively contemporary models and methods developed thus far [1]. To cope with these changing competitive markets, as well as the ability to meet customer demands for increasingly shorter delivery times, and to ensure that the supply can be synchronized to meet the peaks and troughs of the demand are obviously of critical importance [2, 3]. Hence, companies now require a high level of maneuverability encompassing the entire spectrum of activities within an organization. Consequently, agility in addressing new ways to manage enterprises for quick and effective reaction to changing markets, driven by customer-designed products and services, has become the dominant vehicle for competition [4]. Generally, agility benefits can mass customization, increase market share, satisfy customer requirements, facilitate rapid introduction of new products, eliminate non-value-added activities, reduce product costs and increase the competitiveness of enterprises. Accordingly, agility has been advocated as the business paradigm of the 21 st century, being considered the winning strategy for becoming a global leader in an increasingly competitive market of quickly changing customer requirements [5-7]. However, the ability to build agility has not developed as rapidly as anticipated, because the development of technology to manage an agile enterprise is still in progress [4, 6, 8]. Thus, in embracing agility, many important questions must be asked, such as: Precisely what is agility, and how can it be measured? How will companies know when they possess this attribute since no simple metrics or Supply Chain: Theory and Applications 206 indices are available? How and to what degree do the attributes of an enterprise affect its business performance? How does one compare agility with a competitive enterprise? To improve entrepreneurial agility, how does one identify the principal unfavorable factors? How can one assist in more effectively achieving agility [8-10]? Answers to such questions are critical to practitioners and the theory of agile entrepreneurial design. Therefore, the purpose of this research is to seek solutions to some of these problems, with a particular focus on agile strategic planning and measurement, as well as identifying the principal obstacles to improvement of agility. Actually, the purpose of agile strategic planning is to unite the resources of an enterprise and to create business value. Agile enterprises are concerned with change, uncertainty and unpredictability within their business environment and making an appropriate response; therefore, these enterprises require a number of distinguishing attributes to promptly deal with the changes within their environment. Such attributes consist of four principal elements [7, 8]: responsiveness, competency, flexibility/adaptability and quickness/speed. Furthermore, the foundation for agility is comprised of the integration of information technologies, personnel, business process organization, innovation and facilities into strategic competitive attributes. To be truly agile, an enterprise must logically integrate and deploy a number of distinguishing providers with drivers and good capabilities, being finally transformed into strategic competitive edges [11]. Many theoretical models have been proposed for agile enterprise planning [1, 12-15]; however, only a few provide integrated methodologies suitable for adoption to enhance by identifying providers, beginning with the competitive bases of the enterprise. The relationship matrix in the quality function deployment (QFD) method provides an excellent tool for aligning important concepts and linking processes. Moreover, fuzzy logic is a useful tool for capturing the ambiguity and multiplicity of meanings of the linguistic judgments required to express both relationships and rates of agility attributes.To assist managers in more efficiently achieving agility, a systematic methodology, based on fuzzy logic and the relationship matrix in the QFD is devised to provide a means for linking the perspectives from agility drivers with their corresponding capabilities and providers, thereby measuring the agility of an enterprise as well as identifying the principal obstacles to improvement. The remainder of this report is organized as follows. In Section II the related research is reviewed. In section III a conceptual model of an agile enterprise is described in detail for the development of a systematic evaluative methodology in Section IV. The development of a practical case is presented illustrated in Section V. Finally, Section VI a concluding discussion. 2. Review of related research A. Methodology Numerous studies for developing methodologies have been proposed to assist managers in the implementation of strategic planning for achieving agility. For example, to promote a new understanding of cooperation as a vital means of survival and prosperity in the new business era, Preiss et al. [12] proffered a generic model for approaching agility. This model consists of certain steps that can assist an enterprise in understanding its business environment and the changes occurring there, the attributes enabling the infrastructure, and the business processes that should be recognized in the subsequent actions of the organization to sustain its competitive advantage. The first integrated framework to achieve Align Agile Drivers, Capabilities and Providers to Achieve Agility: a Fuzzy-Logic QFD Approach 207 agility was proposed by Gunasekaran [15]. The framework explains how the major capabilities of agile manufacturing should be supported and integrated with appropriate providers to develop an adaptable organization. Seeking to exploit the concept and practices of agility, two research teams [1, 10] have developed a three-step methodology for achieving agility. This methodology provides manufacturing companies with a tool for understanding the total concept of agility, assessing their current positions, determining their need for agility and the capabilities required for achievement, as well as adopting relevant practices which can induce these capabilities. A three-step model was also suggested by Jackson and Johansson [14] to analyze the agility of production systems. Their methodology begins with an assessment of the degree of market turbulence, to determine the relevance of agility in a specific context. Then, the strategic view of the company is examined, with a particular focus on potentials to enhance flexibility and change competencies as viable strategies to achieve a competitive advantage. Although structured frameworks to formulate agility have been identified, most of them for strategic formulation are structural in nature. Thus, to assure that the providers can satisfy the strategic direction of an enterprise, an integrated methodology suitable for adoption to enhance agility by identifying its providers, beginning with competitive bases of the enterprise, is critical to both practitioners and the theory of agile enterprise design. B. Measurement Many approaches to the measurement of agility have been proposed to assist managers in assessment; however, most of these methods assess only the capabilities of agility. Some authors [10, 16, 17] have defined an agility index as a combination of measurement of the intensity levels of enabling attributes; whereas, other measuring methods [18,19] have been developed on the basis of the logical concept of an analytical hierarchical process (AHP). An evaluation index for a mass-customization product manufacturing agility was devised by Yang and Li [20]. Furthermore, to overcome the vagueness of agility assessment, Tsourveloudis and Valavanis [21] designed some IF-THEN rules based on fuzzy logic; moreover, Lin et al. [6] developed a fuzzy agility index (FAI) based on providers using fuzzy logic. Each of these techniques, however, with the exception of the agility providers, seems to address only a limited aspect of a very complicated problem. Although each technique contributes to an understanding of the problem, each - functioning alone - is insufficient for handling the problem in its entirety because the selection of the provider and the assessment should be linked with the drivers and the capabilities [22]. It is therefore necessary to examine the problem from a broader perspective. C. QFD Relationship Matrix The QFD method was designed to emphasize detailed pre-planning to meet customer needs and requirements for new product development. It employs several charts, called house of quality (HOQ), to translate the desires of the customer into the design or engineering characteristics of the product and subsequently into the characteristics of the parts, process plan and production requirements related to its manufacture. Phase I translates the voice of the customer into corresponding engineering characteristics; phase II moves one step backward in the design process by translating the engineering characteristics into characteristics of the parts; phase III identifies the critical process parameters and operations; and finally, phase IV identifies the detailed production requirements. The basic format of the HOQ consists of seven different major components: (1) customer requirements (CRs), (2) importance of customers’ requirements, (3) design requirements (DRs), (4) Supply Chain: Theory and Applications 208 relationship matrix for CRs and DRs, (5) correlation among DRs (6) competitive analysis of competitors, and (7) prioritization of design requirements, as shown in Figure 1. Although QFD has been proposed for customer-driven product development and delivery methodology, an enterprise can achieve various corporate strategic goals such as a reduction in customer complaints, improvement in design reliability and customer satisfaction, easier design change, a reduction in product-development-cycle time, and organizational efficiency by using this method [23, 24]. Similarly, QFD can be extended for aligning drivers with providers to achieve agility and make priority decisions concerning the specific provider improvements that should be made for enhancing the agility level of an enterprise. A simplified form of the HOQ matrix, in which the importance of customers’ requirements, correlation analyses among DRs are removed, is utilized in this study. This simplified form is called a relationship matrix, wherein CRs are represented on the left side. Identifying the relative importance of the various CRs is an important step in discerning those that are critical and also helps in prioritizing the design effort. DRs are represented on the upper portion of the relationship matrix. The relative importance of the DRs can be calculated by using the relative importance of the CRs and the level assigned to the relationships between CRs and DRs, presented in the main body of the matrix, which can be represented in symbolic or numerical form. The level of the relationships is typically assessed by an evaluation team in a subjective manner. D. Fuzzy Logic A fuzzy set can be defined mathematically by assigning a value to each possible member in a universe representing its grade of membership. Membership in the fuzzy set, to a greater or lesser degree, is indicated by a larger or smaller membership grade. Fuzzy-set methods allow uncertain and imprecise systems of the real world to be captured through the use of linguistic terms so that computers can emulate human thought processes. Thus, fuzzy logic is a very powerful tool capable of dealing with decisions involving complex, ambiguous and vague phenomena that can be assessed only by linguistic values rather than by numerical terms. Fuzzy logic enables one to effectively and efficiently quantify imprecise information, perform reasoning processes and make decisions based on vague and incomplete data [25]. On the basis of previous study [26], the experts can make a significant measurement of the possibility of an event when it is known; however, in uncertain situations characterized by either a lack of evidence or the inability of the experts to make a significant measurement when available information is scarce, managers often react very incompetently. Fuzzy logic, by making no global assumptions about the independence, exhaustiveness, or exclusiveness of the underlying evidence, tolerates a blurred boundary in definitions [25]. Thus, fuzzy logic brings the hope of incorporating qualitative factors into decision-making. Fuzzy logic is currently being used extensively in many industrial applications as well as in managerial decision making. For example, it has been used in multi-attribute decision- making situations to select R&D project evaluation [27]. Ben Ghalia et al. [28] used fuzzy- logic inference for estimating hotel-room demand by eliciting knowledge from hotel managers and building fuzzy IF-THEN rules. Lin and Chen [29] devised a fuzzy-possible- success-rating for evaluating go/no-go decisions for new-product screening based on the product-marketing competitive advantages, superiority, technological suitability and risk. Chen and Chiou [30] devised a fuzzy credit rating for commercial loans. Hui et al. [31] obtained data from experienced supervisors to create a fuzzy-rule-based system for balance control of assembly lines in apparel manufacturing. Organizational transformations have Align Agile Drivers, Capabilities and Providers to Achieve Agility: a Fuzzy-Logic QFD Approach 209 been widely adopted by firms to improve competitive advantage. Chu et al. [32] uses a nonadditive fuzzy integral to develop a framework to assess performance of organization transformation. 3. Conceptual model of agile enterprise The goal of an agile enterprise is to enrich/satisfy customers and employees. An enterprise essentially possesses a set of capabilities for making appropriate responses to changes occurring in its business environment. However, the business conditions in which many companies find themselves are characterized by volatile and unpredictable demand; thus, there is an increasing urgency for pursuing agility. Agility might, therefore, be defined as the capability of an enterprise to respond rapidly to changes in the market and customers’ demands. To be truly agile, an enterprise should possess a number of distinguishing agility- providers. From a review of the relevant literature [1, 4, 6, 12, 14], the author has developed a conceptual model of an agile enterprise, as shown in Figure 2. The main driving force behind agility is change. There is nothing new about change; however, change is currently occurring at a much faster rate than ever before. Turbulence and uncertainty in the business environment have become the main causes of failures in enterprises. The number of changes and their type, specification or characteristics cannot be easily determined and probably is indefinite. Different enterprises with dissimilar characteristics and circumstances experience various changes that are specific and perhaps unique to themselves. However, there are some common characteristics in changes that occur, which can produce a general consequence for all enterprises. By summarizing previous studies [1, 4, 7, 8], the general areas of change in a business environment can be categorized as (1) market volatility caused by growth of the market niche, increasing introduction of new product and shrinkage of product life; (2) intense competition caused by rapidly changing markets, pressure from increasing costs, international competitiveness, Internet usage and a short development time for new products; (3) changes in customer requirements caused by demands for customization, increased expectations for quality and quicker delivery time; (4) accelerating technological changes caused by the introduction of new and efficient production facilities and system integration; and (5) changes in social factors caused by environmental protection, workforce/workplace expectations and legal pressure. Agile enterprises are concerned with change, uncertainty and unpredictability within their business environment and making appropriate responses. Therefore, such enterprises require a number of distinguishing capabilities, or “fitness,” to deal with these concerns. These capabilities consist of four principal elements [7, 8]: (1) responsiveness, the ability to see/identify changes, to respond quickly, reactively or proactively, and to recover; (2) competency, the efficiency and effectiveness of an enterprise in reaching its goals; (3) flexibility/adaptability, the ability to implement different processes and achieve different goals with the same facilities; and (4) quickness/speed, the ability to culminate an activity in the shortest possible time. Achieving agility requires responsiveness in strategies, technologies, personnel, business processes and facilities. Agility-providers should exhibit agile characteristics as well as make available and determine the agility capabilities and behavior of an enterprise. Numerous studies dedicated to identifying agility-providers from which organization leaders can select items appropriate to their own strategies, organizational business processes and information Supply Chain: Theory and Applications 210 systems have been conducted. For example, Kumar and Motwani [33] identified twenty- three factors that influence a firm’s agility. Goldman et al. [34] suggested that agility has four underlying components: (1) delivering value to customers, (2) being ready for change, (3) valuing human knowledge and skills, and (4) forming virtual partnerships. The “next generation manufacturing” project identified six attributes for agility: (1) customers, (2) physical plant and equipment, (3) human resources, (4) global markets, (5) core competency, and (6) practices and cultures [35]. Moreover, Yusuf et al. [36] proffered a set of thirty-two agile attributes grouped into four dimensions: (1) core competency management, (2) virtual enterprise, (3) capability for reconfiguration, and (4) knowledge-driven enterprises. These attributes, representing most aspects of agility, determine the entire behavior of an enterprise. Most recently, Ren et al. [37], following the work of Yusuf et al. [36] based on a survey circulated among UK enterprises, conducted principal component analysis to confirm the correlations between the thirty-two attributes. Finally, six principal components encompassing fifteen attributes were identified as critical agility-enabling-attributes: (1) human knowledge and skills, (2) customization, (3) partnership and change, (4) technology, (5) integration and competence, and (6) team-building. From this review we can see that different researchers provide certain insights into different aspects of agility providers. It is highly probable that there is no single set of agility providers reflecting all aspects. Although several researchers [1, 12-15] have accepted a conceptual model for achieve agility, the purpose of agile strategic planning is to unite the resources of an enterprise to compete with the change in environment and to create business value, which according to some studies [4, 22] can be maximized and the competitive threat minimized only by selecting agile providers for investments aligned to the company's business strategy and competitive bases in the market. Thus, the first priority should be to understand the relationships among the specific market field requirement, as well as the agility capabilities and providers, to deploy and integrate both capabilities and providers, and to transform them into a competitive edge. To assist managers in more efficiently achieving agility, on the basis of the conceptual model of an agile enterprise, and by using the relationship matrix in the QFD approach, a systematic model for linking and integrating agility drivers, capabilities and providers, can be constructed as shown in Figure 3. Specifically, this model can be described as follows: x Analysis of agile strategy: to identify the degree of the agile abilities that can provide the required strength for responding to changes and searching for competitive advantage by maintaining alignment between agility drivers and agile abilities. x Identification of agile providers: to find agility providers constituting the means by which the so-called needs of an enterprise relation to capabilities can be achieved by linking between abilities and providers. 4. A fuzzy QFD-based algorithm for evaluation of agility As mentioned in the previous section, the deployment and integration of agility drivers, capabilities and providers, and their transformation into a competitive edge is critical for achieving agility. Due to an either “imprecise” or “vague” definition of agile attributes and relationships, the deploying and integrating evaluation process is associated with uncertainty and complexity. Managers must make a decision by considering agile attributes and relationships which might have non-numerical values. All attributes must be integrated within the evaluation decision although none of them may exactly satisfy the ideals of the [...]... (0.63, 0. 78, 0.93) (0.52 0.70, 0 .87 ) (0.62, 0.77, 0.93) (0.55, 0.73, 0 .89 ) (0.54, 0.72, 0 .88 ) (0.60, 0.75, 0.9) (0.60, 0.76, 0.91) (0.60, 0.75, 0.9) (0.52, 0.71, 0 .88 ) (0.55, 0.73, 0 .89 ) (0.37, 0. 58, 0. 78) Supply Chain: Theory and Applications 226 Agility providers AP1 AP2 AP3 AP4 AP5 AP6 Merits of agility provider (0.3, 0.5, 0.7) (0.5, 0.65, 0 .8) (0.7, 0 .8, 0.9) (0.5, 0.65, 0 .8) (0.5, 0.65, 0 .8) (0.3,... 0.26, 0.4) (0.12, 0. 28, 0.45) (0.09, 0.24, 0.4) (0.07, 0.22, 0.37) (0.13, 0.3, 0. 48) (0.07, 0.23, 0. 38) AP7 (0.5, 0.65, 0 .8) (0.11, 0.27, 0.45) AP8 AP9 AP10 AP11 AP12 AP13 AP14 (0.7, 0 .8, 0.9) (0.5, 0.65, 0 .8) (0.5, 0.65, 0 .8) (0.5, 0.65, 0 .8) (0.5, 0.65, 0 .8) (0.3, 0.5, 0.7) (0.3, 0.5, 0.7) (0.12, 0. 28, 0.46) (0.1, 0.25, 0.4) (0.09, 0.24, 0.4) (0.1, 0.25, 0.4) (0.12, 0.29, 0. 48) (0.11, 0.27, 0.45)... relation-value indexes (0.03, 0.13, 0. 28) (0.06, 0. 182 , 0.36) (0.063, 0.192, 0.36) (0.035, 0.143, 0.296) (0.065, 0.195, 0. 384 ) (0.021, 0.115, 0.266) (0.055, 0.176, 0.36) (0. 084 , 0.224, 0.414) (0.05, 0.163, 0.32) (0.045, 0.156, 0.32) (0.05, 0.163, 0.32) (0.06, 0. 189 , 0. 384 ) (0.033, 0.135, 0.315) (0.066, 0.21, 0.441) Ranking scores 0. 180 8 0.2339 0.2391 0.1929 0.24 78 0.1 681 0.2305 0.2722 0.2115 0.2077 0.2115... of Production Economics, vol 62, pp 7-22, 1999 A Agarwal, R Shankar and M K Tiwari, “Modeling agility of supply chain, ” Industrial Marketing Management, vol 36, pp 443-457, 2007 220 Supply Chain: Theory and Applications Y.Y Yusuf, A Gunasekaran, E O Adeleye and K Sivayoganathan, “Agile supply chain capabilities: Determinants of competitive objectives,” European Journal of Operational Research, vol... no 6, pp 482 - 488 , 2003 A Gunasekaran, “Agile manufacturing: Enables and an implementation framework” International Journal of Production Research, vol 36, no 5, pp 1223-1247, 19 98 M A Youssuf, “The impact of the intensity level of computer-based technologies on quality,” International Journal of Operations & Production Management, vol 14, no 4, pp 4-25, 1993 M M Weber, “Measuring supply chain agility... (EH) (0.5, 0.65, 0 .8) (0.7, 0 .8, 0.9) (0 .85 , 0.95, 1.0) Very Good (VG) Excellent (E) (0.5, 0.65, 0 .8) Relationship-levels Linguistic Fuzzy number variable Very Low (0, 0.1, 0.2) (VL) Low (L) (0.1, 0.25, 0.4) Fair (F) High (H) Very High (VH) (0.3, 0.5, 0.7) (0.6, 0.75, 0.9) (0.7, 0 .8, 0.9) (0 .85 , 0.95, 1.0) Table 1.Fuzzy numbers to approximate linguistic variable values (0 .8, 0.9, 1.0) Align Agile Drivers,... Figure 4 A method for evaluating and achieving agility 229 Supply Chain: Theory and Applications 230 S U(x) SL F SA A HA VA EA 0.7 0 .8 DA 1.0 FAI 0 0.1 0.2 0.3 0.4 0.5 0.6 0.9 1.0 x [(S (0.0, 0.1, 0.2); SL (0.1, 0.2, 0.3); F (0.2, 0.3, 0.4); SA (0.3, 0.4, 0.5); A (0.4, 0.5, 0.6); HA (0.5, 0.6, 0.7); VA (0.6, 0.7, 0 .8) ; EA (0.7, 0 .8, 0.9); DA (0 .8, 0.9, 1.0)] Figure 5 Matching fuzzy agility index with... Prague, Czech Republic, 2001 Supply Chain: Theory and Applications 222 W Karwowski and A Mital, “Applications of approximate reasoning in risk analysis,” in Applications of Fuzzy Set Theory in Human Factors W Karwowski and A Mital Eds, Netherlands, Amsterdam, 1 986 D Dubois and H Prade, Possibility Theory-An Approach to Computerized Processing of Uncertainty New York: Plenum, 1 988 S J Chen and C L Hwang,... skill (0.60, 0.76, 0.91) (0.55, 0.72, 0 .88 ) upgrade (AP2) (0.61, 0.76, 0.92) Technological ability and appropriate product introduction (AC) (0.65, 0.79, 0.93) Cost-effectiveness (AC4) (0.61, 0.77, 0.92) Cooperation and operations efficiency and effectiveness (AC5) Product volume/model flexibility (AC6) Agility providers 225 (0. 58, 0.75, 0.91) (0.54, 0.73, 0 .89 ) Organization/personnel flexibility (AC7)... Product/service design, delivery alacrity and timeliness (AC8) (0.67, 0 .82 , 0.96) Fast operation time (AC9) (0.65, 0 .81 , 0.95) Quick new product introduction (AP3) Response to changing market requirements (AP4) Products with substantial valueaddition (AP5) First-time right design (AP6) Trust-based relations with customers/suppliers (AP7) Technology awareness (AP8) Skill and knowledge enhancement (AP9) Concurrent . choice and allocation: strategic behavior and supply chain performance. Management Science; 45 (8) :1091–1 08. Cachon GP., (1999) “Managing supply chain demand variability with scheduled ordering. Agent-oriented supply- chainmanagement. International Journal of Flexible Manufacturing Systems, 12(2/3), 165– 188 . Fox, M. S., Chionglo J.F., Barbuceanu M., (1993) “The Integrated Supply ChainManagement. Safety Stock Placement in Supply Chains”, Manufacturing and Service Operation Management, Vol. 2, No. 1, pp. 68- 83 . Graves S.C., Willems, S.P., “Optimizing the supply chain configuration for